most citedThe Generative AI Paradox: "What It Can Create, It May Not Understand"

10 citations · 14 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20243 cited

Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement

Jaehun Jung, Faeze Brahman, Yejin Choi

We present a principled approach to provide LLM-based evaluation with a rigorous guarantee of human agreement. We first propose that a reliable evaluation method should not uncriti…

cs.CL2024

How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models

Jaeyoung Lee, Ximing Lu, Jack Hessel +5

Given the growing influx of misinformation across news and social media, there is a critical need for systems that can provide effective real-time verification of news claims. Larg…

cs.CL20241 cited

WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models

Liwei Jiang, Kavel Rao, Seungju Han +8

We introduce WildTeaming, an automatic LLM safety red-teaming framework that mines in-the-wild user-chatbot interactions to discover 5.7K unique clusters of novel jailbreak tactics…

cs.CL2023

STEER: Unified Style Transfer with Expert Reinforcement

Skyler Hallinan, Faeze Brahman, Ximing Lu +3

While text style transfer has many applications across natural language processing, the core premise of transferring from a single source style is unrealistic in a real-world setti…

cs.AI202310 cited

The Generative AI Paradox: "What It Can Create, It May Not Understand"

Peter West, Ximing Lu, Nouha Dziri +11

The recent wave of generative AI has sparked unprecedented global attention, with both excitement and concern over potentially superhuman levels of artificial intelligence: models…